PhD Defence - Masood Zamani

Title: Protein Secondary Structure Prediction Evaluation and a Novel Transition Site Model with New Encoding Schemes

Abstract:

Rapid progress in genomics has led to the discovery of millions of protein sequences while less than 0.2% of the sequenced proteins' structures have been resolved by X-ray crystallography and NMR spectroscopy which are time consuming, complex and expensive. Employing computational techniques for protein structure prediction at secondary and tertiary levels, with regards to advances in computational resources, provides alternative ways to to accelerate the prediction process and overcome the extremely low percentage of protein structure determination. In addition, only a handful of new protein folds have been identified according to the two protein structure classifications databases CATH and SCOP in the last 6 years. State-of-the-art protein secondary structure (PSS) prediction methods employ machine learning (ML) techniques, compared to early approaches based on statistical information and sequence homology. In this research, we develop a two-stage PSS prediction model based on Artificial Neural Networks (ANNs) and Genetic Programming (GP) through a novel framework of PSS transition sites, and new amino acid encoding schemes derived from the genetic Codon mappings, Clustering and Information theory.